Vascular defects associated with hereditary hemorrhagic telangiectasia revealed in patient-derived isogenic iPSCs in 3D vessels on chip

Summary Hereditary hemorrhagic telangiectasia (HHT) is a genetic disease characterized by weak blood vessels. HHT1 is caused by mutations in the ENDOGLIN (ENG) gene. Here, we generated induced pluripotent stem cells (hiPSCs) from a patient with rare mosaic HHT1 with tissues containing both mutant (ENGc.1678C>T) and normal cells, enabling derivation of isogenic diseased and healthy hiPSCs, respectively. We showed reduced ENG expression in HHT1 endothelial cells (HHT1-hiPSC-ECs), reflecting haploinsufficiency. HHT1c.1678C>T-hiPSC-ECs and the healthy isogenic control behaved similarly in two-dimensional (2D) culture, forming functionally indistinguishable vascular networks. However, when grown in 3D organ-on-chip devices under microfluidic flow, lumenized vessels formed in which defective vascular organization was evident: interaction between inner ECs and surrounding pericytes was decreased, and there was evidence for vascular leakage. Organs on chip thus revealed features of HHT in hiPSC-derived blood vessels that were not evident in conventional 2D assays.


INTRODUCTION
Hereditary hemorrhagic telangiectasia (HHT) is an inherited genetic disorder caused by autosomal dominant mutations in Endoglin (ENG; HHT1), Activin receptor like kinase-1 (ACVRL1; HHT2) or SMAD4 (HHT3), genes that mediate signaling by transforming growth factor b (TGF-b) and bone morphogenetic protein (BMP) in vascular endothelial cells (ECs) (Goumans et al., 2009). Phenotypically, HHT causes tortuous defects in blood vessels, particularly evident in the skin and mucous membranes, that are prone to hemorrhage (Govani and Shovlin, 2009). These abnormalities, called telangiectases, consist of enlarged and dilated capillaries that lack the pericyte/smooth muscle cell coverage of normal vessels. Studies in mice indicated that ENG deficiency can lead to abnormal endothelial-pericyte cell interactions caused by defective paracrine signaling by ECs lacking ENG (Carvalho et al., 2004;Lebrin et al., 2010). More severe abnormalities, evident as arteriovenous malformations (AVMs) in the brain, lung, liver, and gastrointestinal tract, can be fatal if hemorrhage occurs (Govani and Shovlin, 2009). To date, there are no therapies that prevent the formation of these abnormalities in patients with HHT or reverse them once they have occurred. At most, current therapies, such as surgical intervention or cauterization of vessels to divert blood flow, ameliorate symptoms of the disease but are not cures (Shovlin, 2010). Medical treatments under investigation include anti-angiogenic, -inflammatory, and -fungal drugs such as humanized anti-vascular endothelial growth factor (VEGF) antibody (bevacizumab) (Dupuis-Girod et al., 2012), thalidomide (Lebrin et al., 2010), itraconazole (Kroon et al., 2020), and other drugs (reviewed in Snodgrass et al., 2021). Genetic models of HHT in mice, in which the genes responsible for the disease in humans are deleted, show clear vascular defects, but they have not shed much light on the specific genotype/phenotype relationships in HHT patients (Tual-Chalot et al., 2015). Attempts to model HHT using primary human umbilical vein ECs (HUVECs) isolated from newborn HHT patients failed to recapitulate the phenotype (Chan et al., 2004). Blood outgrowth ECs (BOECs) or peripheral blood monocytes (PBMCs) from patients with HHT could be alternative sources of cells to model HHT (Begbie et al., 2003;Fernandez-L et al., 2005;Laake et al., 2006), but their poor proliferation in vitro makes them unsuitable as a renewable source of ECs for reproducibly modeling the disease in humans and for drug discovery.
In the present study, we aimed to establish an efficient and scalable system that would recapitulate the formation of defective blood vessels in patients with HHT, based on patient-derived human induced pluripotent stem cells (HHT1-hiPSCs). We hypothesized that HHT1-hiPSCs might be useful for (1)

RESULTS
HHT1-hiPSC-ECs reflect ENG haploinsufficiency with no apparent differences in functionality hiPSC lines were generated from somatic tissue from a patient with HHT1 with a heterozygous nonsense mutation in ENG (NM_001114753.2 (ENG):c.1678C>T; p.(Gln560*)), which causes ENG haploinsufficiency (Letteboer et al., 2005). The patient was identified as being a genetic mosaic, allowing generation of isogenic pairs of hiPSC lines with and without the mutation (HHT1 c.1678C>T and HHT1 WT ) (Figures S1A-S1C; unpublished data). HHT1-hiPSC clones had normal karyotypes and were verified as pluripotent using standard methods (PluriTest, expression of pluripotency markers and spontaneous differentiation toward three germ cell lineages) (Figures S1D-S1G). HHT1-hiPSCs were then induced to differentiate to ECs (Orlova et al., 2014a;2014b). Surface ENG (CD105) was significantly downregulated in HHT1 c.1678C>T -hiPSC-ECs compared with HHT1 WT -hiPSC-ECs ( Figures 1A and 1B). By contrast, surface expression of other major EC markers such as vascular endothelial cadherin (VEC), platelet and endothelial adhesion molecule (CD31/PECAM1), and kinase insert domain receptor (KDR), also known as VEGFR2, was similar among lines ( Figure 1B). ENG haploinsufficiency had no apparent effect on the proliferation of HHT1-hiPSC-ECs ( Figure S2A). HHT1-hiPSC-ECs showed similar responses upon shortterm stimulation with BMP9 and TGF-b, except that ID1 expression was significantly upregulated in HHT1 c.1678C>T -hiPSC-ECs after 2 h of TGF-b treatment ( Figure S2B). Immunostaining showed comparable expression and localization of VEC, CD31, and intracellular von Willebrand factor (vWF) ( Figure 1C). Barrier function was assessed by real-time impedance spectroscopy (electric cellsubstrate impedance sensing [ECIS]) in an integrated assay of electric wound healing for migration, as previously described (Halaidych et al., 2018). Barrier function in two dimension (2D) was similar in healthy and HHT1-hiPSC-ECs in ''complete'' EC growth medium (CGM) ( Figures 1D and 1E), and migration rates were identical (data not shown). Barrier function was increased in growth-factor-free medium (1% platelet-poor plasma serum [PPS]) ( Figures S2C and S2D), in line with our previous findings (Halaidych et al., 2018). BMP9 addition reduced barrier function equally in healthy and HHT1 c.1678C>T -hiPSC-ECs ( Figures S2C and S2D). TGF-b addition significantly reduced barrier function in HHT1 c.1678C>T -hiPSC-ECs but had no significant effect in HHT1 WT -hiPSC-ECs ( Figures S2C and S2D), which is also in line with the differences in responses in HHT1-hiPSC-ECs upon TGF-b treatment. Migration rates were similar in all conditions examined ( Figure S2E).
Finally, the ability to form 2D vascular networks in vitro was examined, as described previously (Halaidych et al., 2018;Orlova et al., 2014aOrlova et al., , 2014b. HHT1-hiPSC-ECs formed wellorganized vascular networks when cultured with stromal cells, and these were indistinguishable from healthy controls. Furthermore, stromal cells adjacent to ECs upregulated expression of the contractile smooth muscle cell marker SM22 ( Figure 1F). Quantification of vascular networks formed by HHT1 c.1678C>T -hiPSC-ECs and HHT1 WT -hiPSC-ECs showed similar total vessel length and number of branches ( Figure 1G) as well as total number of SOX17 + ECs and number of adjacent SM22 + cells (data not shown).

HHT1-hiPSC-ECs show defective vascular organization in 3D microfluidic chips
The ability of HHT1-hiPSC-ECs to form microvascular networks in a 3D vessel-on-chip (VoC) model was then examined ( Figure 2A). Primary human brain vascular pericytes (HBVPs) were used to support microvascular-network formation, as described previously (Vila Cuenca et al., 2021). Vascular networks developed around day 2-3 of culture; lumenized microvessels were observed around day 5 (Figure S3A). Microfluidic chips were immunostained with (C) Immunofluorescent analysis of EC markers VEC, CD31, and vWF on isolated ECs from HHT1 patient-derived isogenic hiPSCs (P2). Scale bar: 75mm. (D) Absolute resistance of the EC monolayer at 4,000 Hz in complete EC growth medium (CGM). ECs differentiated from two independent hiPSC clones were analyzed. Error bars are ± SD of three to five independent biological experiments per clone. (E) Quantification of absolute resistance values at 4,000 Hz from (D). Error bars are shown as ± SD of five independent biological experiments. (F) Representative immunofluorescent images of an in vitro vasculogenesis sprouting assay at day 10 of the co-culture of ECs differentiated from two independent clones of HHT1 patient-derived isogenic hiPSCs and CD31cells differentiated from an independent control hiPSC line. ECs are stained with anti-CD31 (red) and anti-SOX17 (gray), contractile CD31cells with anti-SM22 (green), and nuclei with DAPI (C) Quantification of HHT1-hiPSC-EC vascular network showing vessel density, diameter, number of HHT1-hiPSC-ECs (SOX17+ nuclei), and total vessel length. Data are shown as ± SD, Unpaired t test. ****p < 0.0001, ***p < 0.0005, *p < 0.05, ns, not significant. Normalized (legend continued on next page) antibodies against CD31/PECAM1 and an EC-specific transcription factor SOX17. The ability to form microvascular networks in microfluidic chips was compromised in HHT1 c.1678C>T -hiPSC-ECs compared with HHT1 WT -hiPSC-ECs ( Figures 2B and 2C), despite similar initial seeding densities ( Figure S3A). Quantification of microvascular networks showed reduced vascular density, diameter of the vessels, and number of ECs (SOX17+ nuclei) and an increase of the total length of the vessels of the networks formed by HHT1 c.1678C>T -hiPSC-ECs ( Figures 2C, S3C and S3D). HHT1 WT -hiPSC-ECs from two independent hiPSC clones behaved similarly ( Figure S3B). Furthermore, proliferation of HHT1 c.1678C>T -hiPSC-ECs was lower than HHT1 WT -hiPSC-ECs, as evidenced by fewer EdU-positive nuclei ( Figures S3E and S3F). To demonstrate that the microvascular networks formed by HHT1-hiPSC-ECs were hollow, fluorescent beads were perfused through the vessels (Video S1). Notably, the beads moved at a considerably lower rate in the microvascular networks formed by HHT1 c.1678C>T -hiPSC-ECs compared with HHT1 WT -hiPSC-ECs, indicating reduced flow through 3D vessels formed by HHT1 c.1678C>T -hiPSC-ECs. Junctional integrity was examined by immunostaining of the microvascular networks with VEC and ZO1 ( Figures 2D  and 2E). The results showed that although VEC junctional distribution was comparable, junctional distribution of ZO1 was reduced in the microvascular networks formed by HHT1 c.1678C>T -hiPSC-ECs compared with HHT1 WT -hiPSC-ECs.
We next performed a fluorescent dextran leakage assay to test whether reduced EC-pericyte interaction caused the 3D vessels to be more fragile and prone to leak. Fluorescently labeled dextran (FITC-Dextran, 40 kDa) was added into the medium channel of the organ-on-chip device, and realtime videos of vascular segments pre-stained using fluorescent agglutinin were made ( Figure 4A; Video S2). Quantification of permeability coefficient showed increased leakage of 3D vascular segments formed from HHT1 c.1678C>T -hiPSC-ECs compared with HHT1 WT -hiPSC-ECs ( Figures 4B and 4C).

DISCUSSION
In this study, we developed a human in vitro model for the genetic vascular disorder HHT using hiPSCs derived from patients with mutations in the ENG gene (HHT1). The results showed that we likely captured the direct effects of reduced ENG protein on the EC surface without compensation or adaption mechanisms that normally occur in vivo, notably in mutant mice (El-Brolosy and Stainier, 2017). Thus, even though differences in vessels were observed, aspects of the HHT phenotype were masked although the poor EC-pericyte interaction was similar to that reported previously in Eng +/mutant mice (Galaris et al., 2019). This would contribute to vessel instability and could cause the leaky 3D vascular network formed by HHT1 c.1678C>T -hiPSC-ECs, in line with observations in patients.
The results in the HHT1-hiPSC-EC 2D model differ from earlier studies in which small interfering RNA (siRNA) was used to knock down Eng transiently in mouse embryonic ECs (Lebrin et al., 2004). Complete Eng knockdown in mouse embryonic ECs resulted in reduced EC proliferation and TGF-b signaling in 2D assays. On the other hand, ENG haploinsufficiency had no effect on EC function in 2D, with proliferation, barrier function, and sprouting angiogenesis in ECs derived from HHT1 c.1678C>T -hiPSC clones indistinguishable from isogenic controls.
To establish a HHT1-hiPSC-ECs VoC model, we used a commercially available microfluidic chip that supports formation of an interconnected microvascular network Chen et al., 2017;Shin et al., 2012, Vila Cuenca et al., 2021. The model allows simultaneous analysis of both the early steps of the 3D vascular-network values from independent experiments are shown. From N = 3, n = 9; three independent experiments with three microfluidic channels per experiment (HHT WT#1 , HHT c.1678C>T#1 ). From N = 5, n = 15; five independent experiments with three microfluidic channels per experiment (HHT WT#1 , HHT c.1678C>T#2 ). formation, such as EC cell proliferation, migration, lumen formation, remodeling and pruning (regression), and endpoint analysis. This includes high-resolution microscopy for EC-pericyte interaction, perfusion studies, and vascular leakage assays. Gravity-driven flow in these chips is sufficient for the maintenance of the vascular segments that are perfused with non-perfused vascular segments regressing overtime, similar to what was observed in vivo (Franco et al., 2015;Kochhan et al., 2013).
Overall, we found that HHT1 c.1678C>T -hiPSC-ECs showed multiple similarities to Eng +/mutant mice (Arthur et al., 2000;Carvalho et al., 2004;Lebrin et al., 2010), although there were some differences. These included the formation of narrower vessels with fewer ECs by HHT1 c.1678C>T -hiPSC-ECs than healthy controls. Furthermore, HHT c.1678C>T -hiPSC-ECs showed reduced junctional localization of ZO1, although localization of VEC was comparable. This could be a result of reduced EC-matrix adhesion, which in turn could affect cell-cell junctions (Yamamoto et al., 2015) and reduced perfusion and increased regression of vascular networks, as described previously in eng mutant zebrafish (Sugden et al., 2017), resulting in reduced EC-pericyte interaction. However, the role of ENG in regulation of cell-tocell and cell-to-matrix adhesion is beyond the scope of the present study. Thus, despite some shortcomings of the VoC model in capturing the complete HHT patient phenotype, we believe the model is a valuable tool to investigate underlying causes of poor EC-pericyte interaction and identify drugs to reverse it and mediate vascular normalization.
Additional triggers of AVM formation include somatic mutations that reduce ENG function, local loss of ENG protein caused by inflammation, and pro-angiogenic triggers (Mahmoud et al., 2010;Tual-Chalot et al., 2015). Loss of ENG function in mutant mice was shown to induce defective migration against blood flow and EC enlargement, which caused vessels to dilate (Sugden et al., 2017). This, in turn, results in higher hemody-namic forces and peripheral hypoxia that support the enlargement of AVMs (Sugden and Siekmann, 2018). Our current model mainly addressed ENG haploinsufficiency due to ENG gene defects and lacked the additional triggers that cause ENG loss of function, such as exposure to pro-angiogenic stimuli and hemodynamic force. The particular advantage of using HHT patient-derived hiPSC lines is that they can be engineered to allow inducible ENG knock down or degron-based ENG deletion. We expect that this, in combination with incorporation of pro-inflammatory triggers, such as pro-inflammatory macrophages, into the model will allow complete recapitulation of the phenotype in the future, such that these next-generation models can be implemented in screening for new therapeutic interventions and drug discovery using mechanism-based approaches with opportunities for validation using ECs from patient-derived hiPSCs.
(E) Surface-rendering images processed using Imaris 9.5 software (Bitplane, Oxford Instruments) from spinning disk confocal images showing vascular networks formed by HHT1-hiPSC-ECs (gray; CD31) differentiated from HHT1 WT and HHT1 c.1678C>T hiPSC lines in microfluidic chips. Color code for HBVPs showing green for objects touching the vessel, and color scale representing distance from the vessel. Scale bar: 30 mm. (F) Quantification of average distance of surface-rendered SM22 cells to CD31 surface-rendered objects using IMARIS 9.5 software (Bitplane, Oxford Instruments). From N = 5, n = 7 (wild type [WT]) and n = 6 (mutant [MUT]); five independent experiments with one to three areas of each channel quantified. Error bars are ± SD. Unpaired t test. **p < 0.01. differentiation toward three germ lineages. Sample identity has been confirmed by analysis with the DNA analysis software GeneMarker v.2.6.0 (SoftGenetics, State College, PA, USA) of fragments generated by the AmpFlSTR Profiler Plus PCR Amplification Kit (Applied Biosystems, Foster City, CA, USA) that have been run on a 3730 DNA Analyzer (Applied Biosystems). All tests were performed according to the instructions of the manufacturers.

Statistics
One-way ANOVA and non-parametric Student's t test for unpaired measurements were applied as appropriate to test for differences in means between the groups. Detailed statistics are indicated in each figure legend. Data are expressed and plotted as the mean ± SD. Statistical significance is indicated in each figure legend. Statistical analysis was performed with GraphPad Prism 9.0.2.

Differentiation of hiPSC-ECs
hiPSC differentiation towards endothelial cells and CD31 magnetic bead isolation were performed as described previously (Halaidych et al., 2018;Orlova et al., 2014aOrlova et al., , 2014b. Briefly, hiPSCs were passaged in normal culture conditions one day before inducing differentiation. Mesoderm differentiation was induced by changing the media to B(P)EL with a high concentration CHIR99021 (8 µM). At day 3, 6 and 9 of differentiation the cells were refreshed with B(P)EL with VEGF (50 ng/ml) and SB43152 (10 μM, Tocris). ECs were purified with CD31 coupled magnetic beads at day 10 (Life Technologies) and the culture was further scaled up on 0,1% gelatin coated tissue culture flasks in human endothelial serum free media (EC-SFM)(Life Technologies) with additional VEGF (30 ng/ml), bFGF (20 ng/ml, R&D) and 1% platelet poor serum (PPS)(Hycultec). Functional assays were performed on cells between passages 2-3.

Endothelial cell proliferation (MTS assay)
hiPSC-ECs were seeded into on FN-coated 96-well plates at the seeding density 2,000 cells/well in EC-SFM for 12 h and subsequently refreshed with EC-SFM containing various stimuli. After 4 days the MTS assay (CellTiter, Promega) was used to determine the relative number of ECs.

Assessment of hiPSC-ECs functionality in an in vitro vasculogenesis assay
The co-culture experiments with hiPSC-ECs or primary ECs and stromal cells were performed as described previously (Halaidych et al., 2018;Orlova et al., 2014aOrlova et al., , 2014b. The co-cultures were stopped at day 10 and post-fixed and stained with anti-CD31 (1:200, Dako) and anti-SOX17 (1:200, R&D), and anti-SM22 (1:200, Abcam) antibodies (supplemental table 2). The co-cultures were imaged with the EVOS FL AUTO2 Imaging system (ThermoFischer Scientific) with the 10X Objective for quantifications with autofocus on CD31, and auto stitching 4X4 focus planes or 20X Objective for CD31 and SOX17 images. The co-cultures were quantified using publicly available software AngioTool (Zudaire et al., 2011).

Endothelial barrier function analysis
Endothelial barrier function was measured using impedance-based cell monitoring with an electric cellsubstrate impedance sensing system (ECIS Zθ, Applied Biophysics), as described previously (Halaidych et al., 2018). hiPSC-ECs were seeded on FN-coated ECIS arrays each containing 8 wells with 10 gold electrodes per well (8W10E PET, Applied Biophysics). The cell seeding density was estimated ~50,000cells/cm 2 . For barrier function and migration studies the cells were seeded for at least 24h in complete EC growth medium followed by 6h serum starvation step in EC-SFM. For the assessment of cell migration after serum starvation, the medium was changed to complete EC growth medium or EC-SFM supplemented with 1% PPS, BMP9 (2ng/ml) and TGFb3 (1ng/ml), and electric wound (10 sec pulse of 5V at 60 kHz) was applied to the cells 1h after medium change. Recovery of the barrier was monitored in real time over 6-12h. Multiple frequency/time (MFT) mode was used for the real-time assessment of the barrier and monolayer confluence.

Generation of perfused vascular networks in microfluidic chips
Vascular networks were generated as described previously (Chen et al., 2017) with some adjustments that were developed during optimization of the protocol with hiPSC-ECs and primary human brain vascular pericytes (HBVPs)(ScienceCell), and microfluidic chips with one gel channel and two media channels (AIM Biotech). Cells were resuspended in EGM-2 supplemented with thrombin (4 U/ml) at 10x10 6 cells/ml for hiPSC-ECs and 0.5x10 6 cells/ml for HBVPs or 2x10 6 cells/ml for HBVPs (note: higher HBVP numbers promote vessel formation, although earlier experiments were conducted with a lower number of HBVPs with comparable results). The cell suspension was mixed with an equal volume of fibrinogen solution (10 mg/ml; final concentration 5 mg/ml) and injected into the gel channel of the microfluidic chip; this t was left for 15 min at room temperature (RT) to allow fibrin gel to form. EGM-2 supplemented with VEGF (50 ng/ml) was added to each of the flanking media channels. Interstitial flow through the gel was achieved by adding a larger volume of medium to one of the media inlets, generating a pressure gradient. The microfluidic chips were refreshed every 24 hours with EGM-2 supplemented with VEGF (50 ng/ml) and γ-secretase inhibitor N-[N-(3,5-difluorophenacetyl)-l-alanyl]-s-phenylglycinet-butyl ester (DAPT, 10 µM) (DAPT supplementation was performed on day 1 for 24 hours). For immunofluorescent staining, 3D cultures were fixed with 4% paraformaldehyde (PFA; Sigma) for 20 min at RT, permeabilized with 0.5% TX-100 for 15 min at RT and blocked with 3% bovine serum albumin (BSA) in PBS for 3 hours at RT. Samples were stained by anti-CD31 (1:200, Dako), anti-SOX17(1:200, R&D), and anti-SM22 (1:200, Abcam). Details of antibodies are given in supplemental table 2. Primary antibodies were prepared in 2% BSA and incubated overnight at 4°C and secondary antibodies were prepared in 2% BSA and incubated for 2 hours at RT. Fluorescence images for quantification were acquired using EVOS AUTO2 using 10x magnification objective and high magnification images were acquired with a DragonFly spinning disk (Andor) microscope using 40x magnification objective post-processing, performed and processed using Imaris 9.5 software (Bitplane, Oxford Instruments).

EdU assay for EC proliferation in 3D microfluidic chips
Proliferation was measured using an EdU Click-iT kit Alexa-488 (ThermoFisher Scientific #C10337) according to manufacturer's protocol. Briefly, on day 6 of culture, microfluidic chips were refreshed with EGM-2 (VEGF 50 ng/ml and DAPT 10 µM) additionally supplemented with EdU (1:1000) for 8 hours. Cells were fixed with 4% PFA for 30 minutes, permeabilised with 0.5% TX-100 for 15 minutes at RT. Freshly prepared Click-iT reaction cocktail was added for 30 minutes at RT. Microfluidic chips were washed twice with 3% BSA-PBS and blocked in 3% BSA-PBS for 1-2 hours at RT, followed by co-staining with primary and secondary antibodies.

Quantification of 3D vessels in microfluidic chips
Microfluidic chips were imaged with the EVOS FL AUTO2 or M7000 Imaging system (ThermoFischer Scientific) using the 10X Objective with autofocus either on the cells in phase-contrast mode (for time series experiments) or on CD31 for fixed samples, and automatic image stitching to cover the entire gel channel for quantification of microvascular networks. Parameters were quantified using pipelines developed on the free open source CellProfiler software (https://cellprofiler.org/) (Carpenter et al., 2006). Briefly, for EC nuclei number, pre-processing steps were used to enhance image features and filter non-specific object identification. A Gaussian filter was applied to mural cell images before object identification was used to measure object morphology and interaction with hiPSC-EC network. Filter steps were applied to images of vascular network to reduce non-specific segmentation from cell junction staining and a minimum crossentropy thresholding method was used to produce a binarized image. The binarized images from the CellProfiler output were then analysed using freely available ImageJ software with the plugin (https://imagej.nih.gov/ij/, https://imagej.net/DiameterJ) (Hotaling et al., 2015). For ECM quantification, binarized vessel images were used as masks to ensure Collagen IV staining intensity was quantified only at the vessel regions excluding any background noise. The number of EdU and SOX17 positive nuclei were quantified with a custom-made pipeline in CellProfiler (Carpenter et al., 2006). Additional quantification of the distance between SM22-and CD31 positive cells was obtained using Imaris 9.5 software (Bitplane, Oxford Instruments).

Perfusion assessment in vessels in microfluidic chips
For perfusion assessment, the chip was placed on day 6 in the EVOS AUTO2 with on stage incubator for time-lapse image acquisition. First, basal fluorescence activity was captured before the addition of fluorescent tracers. Next, 70 μl of 40KDa FITC-Dextran (1:1000, Sigma) or 405-beads (1:10, Fluoro-Max Dyed Blue Aqueous Fluorescent Particles, B0200, ThermoFisher Scientific) in EGM-2 was added to one medium port and 50 µl of EGM-2 to all other media ports to induce interstitial gravity flow. Then, images were captured simultaneously at 20 fps using a 10x magnification objective for 30 seconds. For